TAG: A Decentralized Framework for Multi-Agent Hierarchical Reinforcement Learning
This addresses scalability issues in multi-agent systems for AI researchers, though it appears incremental as it builds on existing HRL concepts with a novel framework.
The paper tackles the problem of limited adaptability and scalability in hierarchical reinforcement learning (HRL) by introducing TAG, a decentralized framework for multi-agent HRL that allows hierarchies of arbitrary depth, achieving improved performance over classical multi-agent RL baselines on standard benchmarks.
Hierarchical organization is fundamental to biological systems and human societies, yet artificial intelligence systems often rely on monolithic architectures that limit adaptability and scalability. Current hierarchical reinforcement learning (HRL) approaches typically restrict hierarchies to two levels or require centralized training, which limits their practical applicability. We introduce TAME Agent Framework (TAG), a framework for constructing fully decentralized hierarchical multi-agent systems. TAG enables hierarchies of arbitrary depth through a novel LevelEnv concept, which abstracts each hierarchy level as the environment for the agents above it. This approach standardizes information flow between levels while preserving loose coupling, allowing for seamless integration of diverse agent types. We demonstrate the effectiveness of TAG by implementing hierarchical architectures that combine different RL agents across multiple levels, achieving improved performance over classical multi-agent RL baselines on standard benchmarks. Our results show that decentralized hierarchical organization enhances both learning speed and final performance, positioning TAG as a promising direction for scalable multi-agent systems.